Most procedures yield surrogate data that allow us to obtain a reasonable approximation of the original outcome when used to simulate epidemic spreading processes. The quality of the approximation, however, depends on the surrogate's method. In particular, the information on the data's group structure turns out to play an important role, in line with other results showing its importance in diffusion processes[1]}, [2]}. Using realistic activity timelines of temporal edges also yields better results [2]}. Once group information and realistic timelines are included in the construction of surrogate data, all methods give good results. The largest discrepancies between the original and surrogate data outcomes are obtained at large spreading and recovery parameters. This is not surprising as these parameters correspond to fast processes. In this case, the outcome can depend on the data's details [4]} and temporal structures at short timescales that are not present in the surrogate data. For instance, in school data, temporal edges between classes occur in a synchronized way during the breaks [5]}, creating activity patterns that would need to be put by hand in the surrogate data and thus be contained in some way in the data summary.
